SCoVMod is an explicitly spatial agent-based simulation model that accounts for recorded commuter patterns and additional local movements that are intended to capture non-work interactions such as recreation, shopping, and school. These movements are further modulated by the recorded time-varying mobility statistics, and geographically explicit population age structures. Whilst this does not capture all human movement, we assume that commuting patterns capture a large proportion of the long-range mobility and that local movements at least partially capture the non-commuting population travelling to shops, schools, and other local community movements. This is a similar assumption regarding mobility to the one used in other studies, including e.g. Barnard et al. [1]}. The model also uses deprivation metrics to account for spatial heterogeneity in outcome likelihoods. The model is parameterised against an estimation of of the number of COVID-19 infections according to the observed number of COVID-19 reported cases in each Council Area, using a model for the case ascertainment ratio [2]}. The model parameters were inferred using Approximate Bayesian Computation (ABC) [3]}.
